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Predicting Links in Graphs with Graph Neural Networks and DGL.ai
- Link prediction is one of the fundamental tasks in graph analytics, involving the prediction of connections (or links) between nodes using Graph Neural Networks (GNNs). Constructing GNNs is made easier with Deep Graph Library (DGL.ai).
- We learn how to set up a project, preprocess data, build a model, and evaluate it for link prediction on the Twitch Social Network dataset from the Stanford Network Analysis Project (SNAP).
- GraphSAGE is specifically designed for GNNs to obtain node embeddings that capture both the structure and features of each node within the graph. Using GraphSAGE, we set up a three-convolutional-layer model with dropout enabled after each node feature update and a subsequent MLP predictor that outputs a probability.
- To reduce overfitting, we use binary cross-entropy with logits as the loss function and AUC as the metric to evaluate the model.
- We generate predictions for all possible pairs of nodes, allowing us to identify potential new connections and their probabilities.
- By using a relatively small dataset and DGL.ai, we show an effective way to build a link prediction model for graphs. As graphs scale up to millions or billions of nodes and edges, handling them requires more advanced solutions.
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